Quantile regression using gradient boosted decision trees for daily residential energy load disaggregation
Bibliographic record
Abstract
Abstract The building sector is responsible for approximately one-third of the total energy consumption, worldwide. This sector is undergoing a major digital transformation, buildings being more and more equipped with connected devices such as smart meters and IoT devices. This transformation offers the opportunity to better monitor and optimize building operations. In the province of Quebec (Canada), most buildings are equipped with smart meters providing electricity usage data every 15 minutes. A current major challenge is to disaggregate the different energy use from smart meter data, a discipline called non-intrusive load monitoring in literature. In this work, the aim is to develop and validate a potentially generalizable model for all houses that identifies the daily share of each energy use based on building information, weather data and smart meter data. Input features are selected and ordered using an aggregated score composed of the correlation coefficient, the feature importance given by a decision tree, and the predictive power score. Two modelling methods based on quantile regression are tested: linear regression (LR) and gradient boosted decision trees (GBDT). Compared to ordinary least squares regression, quantile methods inherently provide more robustness and confidence intervals. Both models are trained and validated using separate datasets collected in 8 houses in Canada where metering and sub-metering were performed during a whole year. Results on the test dataset indicate a better performance of the GBDT model, compared to the LR model, with a coefficient of determination of 0.88 (vs. 0.78), a mean absolute error of 6.34 % (vs. 8.89 %) and a maximum absolute error between the actual and predicted values in 95 % of the cases of 17.2 % (vs. 23.1 %).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".